Reinforcement learning (RL) is a common approach for table tennis methods that do not utilize demonstrations. Methods range from framing the problem as a single-step bandit [1]} to temporarily extended policies controlling the robot in joint space [2]} using on-policy RL, to Hierarchical RL (HRL) [3]}. Of particular interest is [4]}, which utilizes muscular soft robots to facilitate safe exploration and learn RL policies from scratch on a real robot.
